Advanced Data Science

Advanced Data Science

  • Training your TensorFlow models in the cloud52:21

    Training your TensorFlow models in the cloud

    TensorFlow is an open-source software library for numerical computation using data flow graphs.

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  • Deep Learning with Keras Cheat Sheet

    Deep Learning with Keras Cheat Sheet

    Keras is a high-level neural networks API developed with a focus on enabling fast experimentation.

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  • A new set of IDE features to help you and your team work better and faster together46:23

    A new set of IDE features to help you and your team work better and faster together

    RStudio Server Pro has a whole new set of features designed to help you and your team work better and faster together.

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  • Thinking inside the box: you can do that inside a data frame?!43:40

    Thinking inside the box: you can do that inside a data frame?!

    The data frame is a crucial data structure in R and, especially, in the tidyverse. Working on a column or a variable is a very natural operation, which is great. But what about row-oriented work?

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  • Creating and Preprocessing a Design Matrix with Recipes55:42

    Creating and Preprocessing a Design Matrix with Recipes

    In this talk, a new package called recipes is shown where the specification of model terms and preprocessing steps can be enumerated sequentially.

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  • Collaboration and time travel- version control with git, github and RStudio48:56

    Collaboration and time travel- version control with git, github and RStudio

    Hadley Wickham presents and demonstrates how understanding git & github will give you two data science superpowers.

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  • Use JavaScript visualization libraries at the R console56:26

    Use JavaScript visualization libraries at the R console

    HTMLwidgets are always hosted within an R package and include all of the source code for their dependencies. This is to ensure that code which depends on widgets doesn’t require an internet connection

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  • Effective frameworks for thinking about data analysis/data science problems in R47:16

    Effective frameworks for thinking about data analysis/data science problems in R

    Hadley Wickham Chief Scientist at RStudio and Adjunct Professor of Statistics at Rice University will discuss broadly an effective framework for thinking about data analysis/data science problems in R

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  • Tidy Evaluation with rlang Cheat Sheet

    Tidy Evaluation with rlang Cheat Sheet

    Tidy Evaluation (Tidy Eval) is a framework for doing non-standard evaluation in R that makes it easier to program with tidyverse functions.

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  • Python with R and Reticulate Cheat Sheet

    Python with R and Reticulate Cheat Sheet

    The reticulate package provides a comprehensive set of tools for interoperability between Python and R.

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  • Factors with forcats Cheat Sheet

    Factors with forcats Cheat Sheet

    This cheatsheet reminds you how to make factors, reorder their levels, recode their values, and more.

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  • Work with Strings Cheat Sheet

    Work with Strings Cheat Sheet

    The stringr package provides an easy to use toolkit for working with strings, i.e. character data, in R.

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  • Apply Functions Cheat Sheet

    Apply Functions Cheat Sheet

    This cheatsheet will remind you how to manipulate lists with purrr as well as how to apply functions iteratively to each element of a list or vector.

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  • Package Development Cheat Sheet

    Package Development Cheat Sheet

    The devtools package makes it easy to build your own R packages, and packages make it easy to share your R code.

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  • Sparklyr Cheat Sheet

    Sparklyr Cheat Sheet

    Sparklyr provides an R interface to Apache Spark, a fast and general engine for processing Big Data.

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